Mapping the use of cardiovascular genetic services in pediatric clinical care: challenges and opportunities for improvement.

Purpose: Clinical genetic testing is increasingly integrated in managing and diagnosing cardiac conditions and disease. It is important to identify ongoing challenges. This study aimed to better understand how genetic testing is integrated into pediatric cardiac care and identify barriers and opportunities for improvement.

Wastewater-based analysis of antimicrobial resistance at UK airports: Evaluating the potential opportunities and challenges.

With 40 million annual passenger flights, airports are key hubs for microbial communities from diverse geographic origins to converge, mix, and distribute. Wastewater derived from airports and aircraft represent both a potential route for the global dispersion of antimicrobial resistant (AMR) organisms and an under-utilised resource for strengthening global AMR surveillance. This study investigates the abundance and diversity of antimicrobial resistance genes (ARGs) in wastewater samples collected from airport terminals (n = 132), aircraft (n = 25), and a connected wastewater treatment plant (n = 11) at three international airports in the UK (London Heathrow, Edinburgh and Bristol).

Metagenomics unveils the role of hospitals and wastewater treatment plants on the environmental burden of antibiotic resistance genes and opportunistic pathogens.

Antimicrobial resistance (AMR) is a global health challenge, with hospitals and wastewater treatment plants (WWTPs) serving as significant pathways for the dissemination of antibiotic resistance genes (ARGs). This study investigates the potential of wastewater-based epidemiology (WBE) as an early warning system for assessing the burden of AMR at the population level. In this comprehensive year-long study, effluent was collected weekly from three large hospitals, and treated and untreated wastewater were collected monthly from three associated community WWTPs.

Bridging minds and machines: Unmasking the limits in text-based automatic personality recognition for enhanced psychology-AI synergy.

Text-based automatic personality recognition (APR) operates at the intersection of artificial intelligence (AI) and psychology to determine the personality of an individual from their text sample. This covert form of personality assessment is key for a variety of online applications that contribute to individual convenience and well-being such as that of chatbots and personal assistants. Despite the availability of good quality data utilizing state-of-the-art AI methods, the reported performance of these recognition systems remains below expectations in comparable areas.